Field of the Disclosure
Embodiments of the present disclosure generally relate to optical flow processing, and more specifically relate to handling perspective magnification in optical flow processing.
Description of the Related Art
Advanced driver assistance systems, or ADAS, are becoming increasingly commonplace in automobiles. These systems provide the driver with the information about the surroundings and potentially automatically intervene with the process of driving to prevent accidents and road fatalities. Three dimensional (3D) scene understanding using image sensors is an elementary step in building this scene understanding. Scene motion understanding using optical flow is a common technique that provides accurate motion estimation. For determining optical flow, one camera is employed to capture temporal sequences of images (video) of the same scene. Pairs of consecutive images are processed to determine the displacement of pixels between consecutive images. The displacement or flow vector information for the pixels can be used to analyze the motion of objects/volume corresponding to the pixels in the captured scene.
The optical flow techniques used in the ADAS are subject to continuously changing perspective due to camera motion through the scene, i.e., an object in temporally ordered images with relative motion toward or away from the camera appears to change size. When cameras with fisheye lenses are used, even lateral motion in the field of view (FOV) can cause an object to appear to change size in temporally ordered images.
Determination of the optical flow of a pixel is fundamentally a “best correspondence” finding problem. That is, given two consecutive images, a query image and a reference image, the problem is to find the best matching position of a pixel in the query image in the reference image. If perspective changes are not accounted for in searching for the best matching pixel, the match may be inaccurate and the inaccuracy may propagate to algorithms that use the optical flow information. One known approach to addressing perspective changes is the Scale Invariant Feature Transform (SIFT). However, SIFT is computationally complex and may be not suitable for real-time optical flow processing in ADAS.